Delete diffusiondet/configuration_diffusiondet.py

diffusiondet/configuration_diffusiondet.py

@@ -1,167 +0,0 @@
-from transformers import PretrainedConfig
-
-from transformers.models.auto import CONFIG_MAPPING
-from transformers.utils.backbone_utils import verify_backbone_config_arguments
-
-from transformers.utils import logging, PushToHubMixin
-
-logger = logging.get_logger(__name__)
-
-class DiffusionDetConfig(PretrainedConfig):
-
-    model_type = "diffusiondet"
-
-    def __init__(
-            self,
-            use_timm_backbone=True,
-            backbone_config=None,
-            num_channels=3,
-            pixel_mean=(123.675, 116.280, 103.530),
-            pixel_std=(58.395, 57.120, 57.375),
-            resnet_out_features=("res2", "res3", "res4", "res5"),
-            resnet_in_features=("res2", "res3", "res4", "res5"),
-            roi_head_in_features=("p2", "p3", "p4", "p5"),
-            fpn_out_channels=256,
-            pooler_resolution=7,
-            sampling_ratio=2,
-            num_proposals=300,
-            num_attn_heads=8,
-            dropout=0.0,
-            dim_feedforward=2048,
-            activation="relu",
-            hidden_dim=256,
-            num_cls=1,
-            num_reg=3,
-            num_heads=6,
-            num_dynamic=2,
-            dim_dynamic=64,
-            class_weight=2.0,
-            giou_weight=2.0,
-            l1_weight=5.0,
-            deep_supervision=True,
-            no_object_weight=0.1,
-            use_focal=True,
-            use_fed_loss=False,
-            alpha=0.25,
-            gamma=2.0,
-            prior_prob=0.01,
-            ota_k=5,
-            snr_scale=2.0,
-            sample_step=1,
-            use_nms=True,
-            swin_size="B",
-            use_swin_checkpoint=False,
-            swin_out_features=(0, 1, 2, 3),
-            optimizer="ADAMW",
-            backbone_multiplier=1.0,
-            backbone='resnet50',
-            use_pretrained_backbone=True,
-            backbone_kwargs=None,
-            dilation=False,
-            **kwargs
-    ):
-        # We default to values which were previously hard-coded in the model. This enables configurability of the config
-        # while keeping the default behavior the same.
-        if use_timm_backbone and backbone_kwargs is None:
-            backbone_kwargs = {}
-            if dilation:
-                backbone_kwargs["output_stride"] = 16
-            backbone_kwargs["out_indices"] = [1, 2, 3, 4]
-            backbone_kwargs["in_chans"] = num_channels
-        # Backwards compatibility
-        elif not use_timm_backbone and backbone in (None, "resnet50"):
-            if backbone_config is None:
-                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
-                backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"])
-            elif isinstance(backbone_config, dict):
-                backbone_model_type = backbone_config.get("model_type")
-                config_class = CONFIG_MAPPING[backbone_model_type]
-                backbone_config = config_class.from_dict(backbone_config)
-            backbone = None
-            # set timm attributes to None
-            dilation = None
-
-        verify_backbone_config_arguments(
-            use_timm_backbone=use_timm_backbone,
-            use_pretrained_backbone=use_pretrained_backbone,
-            backbone=backbone,
-            backbone_config=backbone_config,
-            backbone_kwargs=backbone_kwargs,
-        )
-
-        # Auto mapping
-        self.auto_map = {
-            "AutoConfig": "configuration_diffusiondet.DiffusionDetConfig",
-            "AutoModelForObjectDetection": "modeling_diffusiondet.DiffusionDet"
-        }
-
-        # Backbone.
-        self.use_timm_backbone = use_timm_backbone
-        self.backbone_config = backbone_config
-        self.num_channels = num_channels
-        self.backbone = backbone
-        self.use_pretrained_backbone = use_pretrained_backbone
-        self.backbone_kwargs = backbone_kwargs
-        self.dilation = dilation
-        self.fpn_out_channels = fpn_out_channels
-
-        # Model.
-        self.pixel_mean = pixel_mean
-        self.pixel_std = pixel_std
-        self.resnet_out_features = resnet_out_features
-        self.resnet_in_features = resnet_in_features
-        self.roi_head_in_features = roi_head_in_features
-        self.pooler_resolution = pooler_resolution
-        self.sampling_ratio = sampling_ratio
-        self.num_proposals = num_proposals
-
-        # RCNN Head.
-        self.num_attn_heads = num_attn_heads
-        self.dropout = dropout
-        self.dim_feedforward = dim_feedforward
-        self.activation = activation
-        self.hidden_dim = hidden_dim
-        self.num_cls = num_cls
-        self.num_reg = num_reg
-        self.num_heads = num_heads
-
-        # Dynamic Conv.
-        self.num_dynamic = num_dynamic
-        self.dim_dynamic = dim_dynamic
-
-        # Loss.
-        self.class_weight = class_weight
-        self.giou_weight = giou_weight
-        self.l1_weight = l1_weight
-        self.deep_supervision = deep_supervision
-        self.no_object_weight = no_object_weight
-
-        # Focal Loss.
-        self.use_focal = use_focal
-        self.use_fed_loss = use_fed_loss
-        self.alpha = alpha
-        self.gamma = gamma
-        self.prior_prob = prior_prob
-
-        # Dynamic K
-        self.ota_k = ota_k
-
-        # Diffusion
-        self.snr_scale = snr_scale
-        self.sample_step = sample_step
-
-        # Inference
-        self.use_nms = use_nms
-
-        # Swin Backbones
-        self.swin_size = swin_size
-        self.use_swin_checkpoint = use_swin_checkpoint
-        self.swin_out_features = swin_out_features
-
-        # Optimizer.
-        self.optimizer = optimizer
-        self.backbone_multiplier = backbone_multiplier
-
-        self.num_labels = 80
-
-        super().__init__()